OpenRaiser/Intern-Atlas huggingface dataset:
Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination
Venue: arXiv cs.CV 2025
Year: 2025

Understanding text-rich videos requires reading small, transient textual cues that often demand repeated inspection. Yet most video QA models rely on single-pass perception over fixed frames, leading to hallucinations and failures on fine-grained evidence. Inspired by how humans pause, zoom, and re-read critical regions, we introduce Video-R4 (Reinforcing Text-Rich Video Reasoning with Visual Rumination), a video reasoning LMM that performs visual rumination: iteratively selecting frames, zooming into informative regions, re-encoding retrieved pixels, and updating its reasoning state. We construct two datasets with executable rumination trajectories: Video-R4-CoT-17k for supervised practice and Video-R4-RL-30k for reinforcement learning. We propose a multi-stage rumination learning framework that progressively finetunes a 7B LMM to learn atomic and mixing visual operations via SFT and GRPO-based RL. Video-R4-7B achieves state-of-the-art results on M4-ViteVQA and further generalizes to multi-page document QA, slides QA, and generic video QA, demonstrating that iterative rumination is an effective paradigm for pixel-grounded multimodal reasoning. Project Page: https://yunlong10.github.io/Video-R4/
How to Expand a Self-orthogonal Code
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we show how to expand Euclidean/Hermitian self-orthogonal code preserving their orthogonal property. Our results show that every $k$-dimension Hermitian self-orthogonal code is contained in a $(k+1)$-dimensional Hermitian self-orthogonal code. Also, for $k< n/2-1$, every $[n,k]$ Euclidean self-orthogonal code is contained in an $[n,k+1]$ Euclidean self-orthogonal code. Moreover, for $k=n/2-1$ and $p=2$, we can also fulfill the expanding process. But for $k=n/2-1$ and $p$ odd prime, the expanding process can be fulfilled if and only if an extra condition must be satisfied. We also propose two feasible algorithms on these expanding procedures.
AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks
Venue: arXiv cs.NI 2025
Year: 2025

Next-generation (NextG) cellular networks are expected to manage dynamic traffic while sustaining high performance. Large language models (LLMs) provide strategic reasoning for 6G planning, but their computational cost and latency limit real-time use. Multi-agent reinforcement learning (MARL) supports localized adaptation, yet coordination at scale remains challenging. We present AURA, a framework that integrates cloud-based LLMs for high-level planning with base stations modeled as MARL agents for local decision-making. The LLM generates objectives and subgoals from its understanding of the environment and reasoning capabilities, while agents at base stations execute these objectives autonomously, guided by a trust mechanism that balances local learning with external input. To reduce latency, AURA employs batched communication so that agents update the LLM's view of the environment and receive improved feedback. In a simulated 6G scenario, AURA improves resilience, reducing dropped handoff requests by more than half under normal and high traffic and lowering system failures. Agents use LLM input in fewer than 60\% of cases, showing that guidance augments rather than replaces local adaptability, thereby mitigating latency and hallucination risks. These results highlight the promise of combining LLM reasoning with MARL adaptability for scalable, real-time NextG network management.
Beyond Awareness: Investigating How AI and Psychological Factors Shape Human Self-Confidence Calibration
Venue: arXiv cs.HC 2025
Year: 2025

Human-AI collaboration outcomes depend strongly on human self-confidence calibration, which drives reliance or resistance toward AI's suggestions. This work presents two studies examining whether calibration of self-confidence before decision tasks, low versus high levels of Need for Cognition (NFC), and Actively Open-Minded Thinking (AOT), leads to differences in decision accuracy, self-confidence appropriateness during the tasks, and metacognitive perceptions (global and affective). The first study presents strategies to identify well-calibrated users, also comparing decision accuracy and the appropriateness of self-confidence across NFC and AOT levels. The second study investigates the effects of calibrated self-confidence in AI-assisted decision-making (no AI, two-stage AI, and personalized AI), also considering different NFC and AOT levels. Our results show the importance of human self-confidence calibration and psychological traits when designing AI-assisted decision systems. We further propose design recommendations to address the challenge of calibrating self-confidence and supporting tailored, user-centric AI that accounts for individual traits.
First Contact with Dark Patterns and Deceptive Designs in Chinese and Japanese Free-to-Play Mobile Games
Venue: arXiv cs.HC 2025
Year: 2025

Mobile games have gained immense popularity due to their accessibility, allowing people to play anywhere, anytime. Dark patterns and deceptive designs (DPs) have been found in these and other gaming platforms within certain cultural contexts. Here, we explored DPs in the onboarding experiences of free-to-play mobile games from China and Japan. We identified several unique patterns and mapped their relative prevalence. We also found that game developers often employ combinations of DPs as a strategy ("DP Combos") and use elements that, while not inherently manipulative, can enhance the impact of known patterns ("DP Enhancers"). Guided by these findings, we then developed an enriched ontology for categorizing deceptive game design patterns into classes and subclasses. This research contributes to understanding deceptive game design patterns and offers insights for future studies on cultural dimensions and ethical game design in general.
Serv-Drishti: An Interactive Serverless Function Request Simulation Engine and Visualiser
Venue: arXiv cs.NI 2025
Year: 2025

The rapid adoption of serverless computing necessitates a deeper understanding of its underlying operational mechanics, particularly concerning request routing, cold starts, function scaling, and resource management. This paper presents Serv-Drishti, an interactive, open-source simulation tool designed to demystify these complex behaviours. Serv-Drishti simulates and visualises the journey of a request through a representative serverless platform, from the API Gateway and intelligent Request Dispatcher to dynamic Function Instances on resource-constrained Compute Nodes. Unlike simple simulators, Serv-Drishti provides a robust framework for comparative analysis. It features configurable platform parameters, multiple request routing and function placement strategies, and a comprehensive failure simulation module. This allows users to not only observe but also rigorously analyse system responses under various loads and fault conditions. The tool generates real-time performance graphs and provides detailed data exports, establishing it as a valuable resource for research, education, and the design analysis of serverless architectures.
Safe Farming: Development of a Prevention System to Mitigate Vertebrates Crop Raiding
Venue: arXiv cs.NI 2025
Year: 2025

One of the main problems for farmers is the protection of their crops, before and after harvesting, from animals and birds. To overcome this problem, this paper proposes a model of safe farming in which the crops will be protected from vertebrates attack through a prevention system that is based on Wirelesses Sensors Networks. Different sensor nodes are placed around the field that detect animals or birds existence and generate required signals and information. This information is passed to the Repelling and Notifying System (RNS) that is installed at the field through a short range wireless technology, ZigBee. As RNS receives the information, it generates ultrasonic sounds that are unbearable for animals and birds, which causes them to run away from the field. These ultrasonic sounds are generated in a frequency range that only animals and birds can hear, while humans cannot notice the sound. The paper also proposes a notifying system. It will inform the farmer about animals or birds intrusion in the field through SMS, but doesn't need any action from the farmer. The low cost and power efficiency of the proposed system is a key advantage for developing countries where cost and power are major players in any system feasibility.
Joint Edge Server Deployment and Computation Offloading: A Multi-Timescale Stochastic Programming Framework
Venue: arXiv cs.NI 2025
Year: 2025

Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, by bringing computation capability closer to end-users at the network edge. In this work, we investigate the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under the stochastic information scenario. Traditional approaches often treat these decisions as equal, disregarding the differences in information realization. However, in practice, the ES deployment decision must be made in advance and remain unchanged, prior to the complete realization of information, whereas the decisions regarding service placement and computation task offloading can be made and adjusted in real-time after information is fully realized. To address such temporal coupling between decisions and information realization, we introduce the stochastic programming (SP) framework, which involves a strategic-layer for deciding ES deployment based on (incomplete) stochastic information and a tactical-layer for deciding service placement and task offloading based on complete information realization. The problem is challenging due to the different timescales of two layers' decisions. To overcome this challenge, we propose a multi-timescale SP framework, which includes a large timescale (called period) for strategic-layer decision-making and a small timescale (called slot) for tactical-layer decision making. Moreover, we design a Lyapunov-based algorithm to solve the tactical-layer problem at each time slot, and a Markov approximation algorithm to solve the strategic-layer problem in every time period.
Q-Learning-Based Time-Critical Data Aggregation Scheduling in IoT
Venue: arXiv cs.NI 2025
Year: 2025

Time-critical data aggregation in Internet of Things (IoT) networks demands efficient, collision-free scheduling to minimize latency for applications like smart cities and industrial automation. Traditional heuristic methods, with two-phase tree construction and scheduling, often suffer from high computational overhead and suboptimal delays due to their static nature. To address this, we propose a novel Q-learning framework that unifies aggregation tree construction and scheduling, modeling the process as a Markov Decision Process (MDP) with hashed states for scalability. By leveraging a reward function that promotes large, interference-free batch transmissions, our approach dynamically learns optimal scheduling policies. Simulations on static networks with up to 300 nodes demonstrate up to 10.87% lower latency compared to a state-of-the-art heuristic algorithm, highlighting its robustness for delay-sensitive IoT applications. This framework enables timely insights in IoT environments, paving the way for scalable, low-latency data aggregation.
Energy Efficiency in Network Slicing: Survey and Taxonomy
Venue: arXiv cs.NI 2025
Year: 2025

Network Slicing (NS) is a fundamental feature of 5G, 6G, and future mobile networks, enabling logically isolated virtual networks over shared infrastructure. As data demand increases and services diversify, ensuring Energy Efficiency (EE) in NS is vital (not only for operational cost savings but also to reduce the Information and Communication Technology (ICT) sector's environmental footprint). This survey addresses the need for a comprehensive and holistic perspective on energy-efficient NS by reviewing and classifying recent strategies across the NS life cycle. Our contributions are threefold: (i) a thorough review of state-of-the-art techniques aimed at reducing energy consumption in NS; (ii) a novel taxonomy that organizes strategies into infrastructure, path/route, and slice operation levels; and (iii) the identification of open challenges and research directions, with a focus on systemic, cross-layer, and AI-driven approaches. By consolidating insights from recent developments, our work bridges existing gaps in the literature, offering a structured foundation for researchers and practitioners to design, evaluate, and improve energy-efficient network slicing systems.
HiFiNet: Hierarchical Fault Identification in Wireless Sensor Networks via Edge-Based Classification and Graph Aggregation
Venue: arXiv cs.NI 2025
Year: 2025

Wireless Sensor Networks (WSN) are the backbone of essential monitoring applications, but their deployment in unfavourable conditions increases the risk to data integrity and system reliability. Traditional fault detection methods often struggle to effectively balance accuracy and energy consumption, and they may not fully leverage the complex spatio-temporal correlations inherent in WSN data. In this paper, we introduce HiFiNet, a novel hierarchical fault identification framework that addresses these challenges through a two-stage process. Firstly, edge classifiers with a Long Short-Term Memory (LSTM) stacked autoencoder perform temporal feature extraction and output initial fault class prediction for individual sensor nodes. Using these results, a Graph Attention Network (GAT) then aggregates information from neighboring nodes to refine the classification by integrating the topology context. Our method is able to produce more accurate predictions by capturing both local temporal patterns and network-wide spatial dependencies. To validate this approach, we constructed synthetic WSN datasets by introducing specific, predefined faults into the Intel Lab Dataset and NASA's MERRA-2 reanalysis data. Experimental results demonstrate that HiFiNet significantly outperforms existing methods in accuracy, F1-score, and precision, showcasing its robustness and effectiveness in identifying diverse fault types. Furthermore, the framework's design allows for a tunable trade-off between diagnostic performance and energy efficiency, making it adaptable to different operational requirements.
Leibniz's Monadology as Foundation for the Artificial Age Score: A Formal Architecture for Al Memory Evaluation
Venue: arXiv cs.AI 2025
Year: 2025

This paper develops a mathematically rigorous, philosophically grounded framework for evaluating artificial memory systems, rooted in the metaphysical structure of Leibniz's Monadology. Building on a previously formalized metric, the Artificial Age Score (AAS), the study maps twenty core propositions from the Monadology to an information-theoretic architecture. In this design, each monad functions as a modular unit defined by a truth score, a redundancy parameter, and a weighted contribution to a global memory penalty function. Smooth logarithmic transformations operationalize these quantities and yield interpretable, bounded metrics for memory aging, representational stability, and salience. Classical metaphysical notions of perception, apperception, and appetition are reformulated as entropy, gradient dynamics, and internal representation fidelity. Logical principles, including the laws of non-contradiction and sufficient reason, are encoded as regularization constraints guiding memory evolution. A central contribution is a set of first principles proofs establishing refinement invariance, structural decomposability, and monotonicity under scale transformation, aligned with the metaphysical structure of monads. The framework's formal organization is structured into six thematic bundles derived from Monadology, aligning each mathematical proof with its corresponding philosophical domain. Beyond evaluation, the framework offers a principled blueprint for building Al memory architectures that are modular, interpretable, and provably sound.
SCARE: A Benchmark for SQL Correction and Question Answerability Classification for Reliable EHR Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in Large Language Models (LLMs) have enabled the development of text-to-SQL models that allow clinicians to query structured data stored in Electronic Health Records (EHRs) using natural language. However, deploying these models for EHR question answering (QA) systems in safety-critical clinical environments remains challenging: incorrect SQL queries-whether caused by model errors or problematic user inputs-can undermine clinical decision-making and jeopardize patient care. While prior work has mainly focused on improving SQL generation accuracy or filtering questions before execution, there is a lack of a unified benchmark for evaluating independent post-hoc verification mechanisms (i.e., a component that inspects and validates the generated SQL before execution), which is crucial for safe deployment. To fill this gap, we introduce SCARE, a benchmark for evaluating methods that function as a post-hoc safety layer in EHR QA systems. SCARE evaluates the joint task of (1) classifying question answerability (i.e., determining whether a question is answerable, ambiguous, or unanswerable) and (2) verifying or correcting candidate SQL queries. The benchmark comprises 4,200 triples of questions, candidate SQL queries, and expected model outputs, grounded in the MIMIC-III, MIMIC-IV, and eICU databases. It covers a diverse set of questions and corresponding candidate SQL queries generated by seven different text-to-SQL models, ensuring a realistic and challenging evaluation. Using SCARE, we benchmark a range of approaches-from two-stage methods to agentic frameworks. Our experiments reveal a critical trade-off between question classification and SQL error correction, highlighting key challenges and outlining directions for future research.
Enhancing Robustness of Offline Reinforcement Learning Under Data Corruption via Sharpness-Aware Minimization
Venue: arXiv cs.LG 2025
Year: 2025

Offline reinforcement learning (RL) is vulnerable to real-world data corruption, with even robust algorithms failing under challenging observation and mixture corruptions. We posit this failure stems from data corruption creating sharp minima in the loss landscape, leading to poor generalization. To address this, we are the first to apply Sharpness-Aware Minimization (SAM) as a general-purpose, plug-and-play optimizer for offline RL. SAM seeks flatter minima, guiding models to more robust parameter regions. We integrate SAM into strong baselines for data corruption: IQL, a top-performing offline RL algorithm in this setting, and RIQL, an algorithm designed specifically for data-corruption robustness. We evaluate them on D4RL benchmarks with both random and adversarial corruption. Our SAM-enhanced methods consistently and significantly outperform the original baselines. Visualizations of the reward surface confirm that SAM finds smoother solutions, providing strong evidence for its effectiveness in improving the robustness of offline RL agents.
Llamazip: Leveraging LLaMA for Lossless Text Compression and Training Dataset Detection
Venue: arXiv cs.LG 2025
Year: 2025

This work introduces Llamazip, a novel lossless text compression algorithm based on the predictive capabilities of the LLaMA3 language model. Llamazip achieves significant data reduction by only storing tokens that the model fails to predict, optimizing storage efficiency without compromising data integrity. Key factors affecting its performance, including quantization and context window size, are analyzed, revealing their impact on compression ratios and computational requirements. Beyond compression, Llamazip demonstrates the potential to identify whether a document was part of the training dataset of a language model. This capability addresses critical concerns about data provenance, intellectual property, and transparency in language model training.
Comparative Analysis of Large Language Model Inference Serving Systems: A Performance Study of vLLM and HuggingFace TGI
Venue: arXiv cs.LG 2025
Year: 2025

The deployment of Large Language Models (LLMs) in production environments requires efficient inference serving systems that balance throughput, latency, and resource utilization. This paper presents a comprehensive empirical evaluation of two prominent open-source LLM serving frameworks: vLLM and HuggingFace Text Generation Inference (TGI). We benchmark these systems across multiple dimensions including throughput performance, end-to-end latency, GPU memory utilization, and scalability characteristics using LLaMA-2 models ranging from 7B to 70B parameters. Our experiments reveal that vLLM achieves up to 24x higher throughput than TGI under high-concurrency workloads through its novel PagedAttention mechanism, while TGI demonstrates lower tail latencies for interactive single-user scenarios. We provide detailed performance profiles for different deployment scenarios and offer practical recommendations for system selection based on workload characteristics. Our findings indicate that the choice between these frameworks should be guided by specific use-case requirements: vLLM excels in high-throughput batch processing scenarios, while TGI is better suited for latency-sensitive interactive applications with moderate concurrency.
Beyond Surface-Level Similarity: Hierarchical Contamination Detection for Synthetic Training Data in Foundation Models
Venue: arXiv cs.LG 2025
Year: 2025

Synthetic data has become essential for training foundation models, yet benchmark contamination threatens evaluation integrity. Although existing detection methods identify token-level overlap, they fail to detect semantic-level contamination where synthetic data conceptually resemble benchmarks without lexical overlap. This gap is critical as foundation models increasingly train on synthetic data that may implicitly encode benchmark knowledge. We propose a hierarchical contamination detection framework operating at four levels: token level, semantic level, reasoning pattern, and performance cliff detection. Through controlled experiments on MMLU, GSM8K and HumanEval, we demonstrate that semantic-level contamination evades existing methods (F1=0.17-0.49) but is effectively detected by our hierarchical approach (F1 = 0.76), with an average improvement of 26. 5\% over state-of-the-art baselines. Our framework provides practitioners with practical tools for audit pipelines and enables responsible deployment of synthetic training data.